Comprehensive analysis of non-motor symptoms and their association with quality of life in Writer's cramp
Bibliographic record
Abstract
OBJECTIVE: Limited studies have focused on non-motor symptoms (NMS) in patients with Writer's cramp (WC). The current study aimed to examine the frequency of NMS and their association with health-related quality of life (HR-QoL) in patients with WC. METHODS: A total of 80 patients with WC and 69 healthy controls (HCs) were enrolled. Motor symptoms was assessed by Burke-Fahn-Marsden dystonia rating scale and NMS was evaluated through several specific scales, including Hamilton depression rating scale-24 items (HDRS-24), Hamilton anxiety rating scale, Epworth sleepiness scale, Pittsburgh sleep quality index, fatigue severity scale (FSS), numerical rating scale for pain, and Montreal cognitive assessment. The HR-QoL was assessed using 36-item short form health survey (SF-36), which can be divided into physical component summary (PCS) and mental component summary (MCS). Multiple linear regression was used to analysis the association between each NMS and HR-QoL. RESULTS: The patients presented more symptoms of depression, anxiety, poor quality of sleep, excessive daytime sleepiness, and fatigue than the HCs. The most frequent NMS in patients with WC was anxiety (51.25%) and depression (46.25%) symptoms. We found that motor symptoms had no association with HR-QoL. Higher scores of HDRS-24 were associated with lower scores of SF-36 in PCS and MCS. Meantime, higher scores of FSS were significantly associated with lower scores of SF-36 in MCS. CONCLUSION: The NMS was prevalent in patients with WC, with frequent anxiety and depression symptoms. Depression symptoms and fatigue had a strongly negative impact on HR-QoL and deserve attention in clinic practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".